Teachers’ Linguistic Politeness in Classroom Interaction: A Pragmatic Analysis
Bibliographic record
Abstract
This study aimed to uncover the different structures of linguistic politeness used in the utterances of the teachers in classroom interaction. More specifically, the analysis made use of House and Kasper’s (1981) Politeness Linguistic Expressions, Brown and Levinson’s (1987) Politeness Strategies, and Leech’s (1983) Politeness Maxims. Using observation and interview, several structures of linguistic politeness were unearthed. Firstly, the politeness linguistic expressions involved politeness markers, consultative devices, downtoners, committers, forewarning, hesitators, and agent avoider. Secondly, the politeness strategies involved positive politeness, negative politeness, off-record strategy, and bald-on record strategy. Lastly, the politeness maxims involved tact, approbation, modesty, and agreement maxim. Politeness is a non-value-laden linguistic phenomenon where it does not always mean what people in the here-and-now take it to mean, but there can always be a conventional ways of expressing so in a particular social interaction. The structures of linguistic politenesss do not always lead to conflict-avoidance, but they only contribute to the success of the effect of the expressions used. Hence, whatever may seem to have been considered as conventionally conventionalized or non-conventionalized politeness in a context, several factors must need to be considered for an expression to be a form of politeness strategy that performs supportive facework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".